| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1914 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "perfectly" | | 1 | "sharply" | | 2 | "quickly" | | 3 | "slightly" | | 4 | "gently" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1914 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "familiar" | | 1 | "echo" | | 2 | "pulsed" | | 3 | "throb" | | 4 | "warmth" | | 5 | "footsteps" | | 6 | "perfect" | | 7 | "pulse" | | 8 | "trembled" | | 9 | "vibrated" | | 10 | "traced" | | 11 | "could feel" | | 12 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 259 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 259 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 262 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1912 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.84% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1881 | | uniqueNames | 13 | | maxNameDensity | 1.06 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Carter | 1 | | Richmond | 3 | | Park | 3 | | Kensington | 1 | | Golden | 1 | | Empress | 1 | | Silas | 1 | | Hel | 2 | | Tonight | 1 | | October | 1 | | Caller | 1 | | Rory | 20 | | Knock | 6 |
| | persons | | | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Kensington" | | 3 | "Golden" | | 4 | "Hel" |
| | globalScore | 0.968 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 142 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a summons" | | 1 | "seemed farther away now, stretched thin by distance" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.523 | | wordCount | 1912 | | matches | | 0 | "Not its usual soft throb, but a quick, insistent beat" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 262 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 163 | | mean | 11.73 | | std | 12.67 | | cv | 1.08 | | sampleLengths | | 0 | 12 | | 1 | 69 | | 2 | 11 | | 3 | 2 | | 4 | 17 | | 5 | 3 | | 6 | 18 | | 7 | 42 | | 8 | 6 | | 9 | 47 | | 10 | 43 | | 11 | 8 | | 12 | 37 | | 13 | 10 | | 14 | 16 | | 15 | 5 | | 16 | 40 | | 17 | 3 | | 18 | 5 | | 19 | 22 | | 20 | 12 | | 21 | 1 | | 22 | 5 | | 23 | 22 | | 24 | 29 | | 25 | 8 | | 26 | 2 | | 27 | 21 | | 28 | 6 | | 29 | 1 | | 30 | 3 | | 31 | 2 | | 32 | 21 | | 33 | 1 | | 34 | 1 | | 35 | 1 | | 36 | 6 | | 37 | 28 | | 38 | 15 | | 39 | 3 | | 40 | 11 | | 41 | 4 | | 42 | 31 | | 43 | 11 | | 44 | 6 | | 45 | 1 | | 46 | 32 | | 47 | 1 | | 48 | 3 | | 49 | 43 |
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| 99.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 259 | | matches | | 0 | "were shut" | | 1 | "were reflected" | | 2 | "was lit" | | 3 | "been sent" |
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| 98.68% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 329 | | matches | | 0 | "was following" | | 1 | "was being" | | 2 | "was looking" | | 3 | "was looking" | | 4 | "was walking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 262 | | ratio | 0.004 | | matches | | 0 | "Not much—perhaps a foot—but enough to make the crown of it disappear beyond the line of her sight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 336 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.017857142857142856 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.002976190476190476 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 262 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 262 | | mean | 7.3 | | std | 5.28 | | cv | 0.723 | | sampleLengths | | 0 | 12 | | 1 | 27 | | 2 | 6 | | 3 | 13 | | 4 | 23 | | 5 | 11 | | 6 | 2 | | 7 | 2 | | 8 | 15 | | 9 | 3 | | 10 | 4 | | 11 | 14 | | 12 | 15 | | 13 | 8 | | 14 | 19 | | 15 | 6 | | 16 | 11 | | 17 | 10 | | 18 | 11 | | 19 | 9 | | 20 | 6 | | 21 | 5 | | 22 | 33 | | 23 | 5 | | 24 | 8 | | 25 | 9 | | 26 | 8 | | 27 | 20 | | 28 | 10 | | 29 | 9 | | 30 | 7 | | 31 | 5 | | 32 | 17 | | 33 | 4 | | 34 | 19 | | 35 | 3 | | 36 | 5 | | 37 | 2 | | 38 | 1 | | 39 | 19 | | 40 | 6 | | 41 | 6 | | 42 | 1 | | 43 | 5 | | 44 | 3 | | 45 | 7 | | 46 | 12 | | 47 | 6 | | 48 | 12 | | 49 | 3 |
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| 39.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 27 | | diversityRatio | 0.29118773946360155 | | totalSentences | 261 | | uniqueOpeners | 76 | |
| 77.16% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 216 | | matches | | 0 | "Then, somewhere to her left," | | 1 | "Then came three soft knocks" | | 2 | "Instead, she put the phone" | | 3 | "Then the knocking began." | | 4 | "Then she saw the light" |
| | ratio | 0.023 | |
| 97.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 66 | | totalSentences | 216 | | matches | | 0 | "She stopped beneath the last" | | 1 | "Her voice went nowhere." | | 2 | "It seemed to sink into" | | 3 | "She had tried ignoring it." | | 4 | "She had delivered a curry" | | 5 | "She had told herself she" | | 6 | "She had told herself she" | | 7 | "She stepped beyond the light." | | 8 | "She took out her phone" | | 9 | "She had walked farther than" | | 10 | "It was also the first" | | 11 | "She turned in a slow" | | 12 | "It was a small sound." | | 13 | "Her hand tightened around the" | | 14 | "She knew trees made sounds." | | 15 | "She knew animals nested in" | | 16 | "She did not know what" | | 17 | "She looked down." | | 18 | "She followed it because staying" | | 19 | "Her footsteps sounded too loud," |
| | ratio | 0.306 | |
| 78.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 165 | | totalSentences | 216 | | matches | | 0 | "Aurora Carter knew Richmond Park" | | 1 | "The gates were shut, the" | | 2 | "A stand of trees that" | | 3 | "She stopped beneath the last" | | 4 | "The battery, which had been" | | 5 | "Her voice went nowhere." | | 6 | "It seemed to sink into" | | 7 | "Rory slipped the phone into" | | 8 | "The silver chain was cold" | | 9 | "The deep-crimson stone rested beneath" | | 10 | "That was why she had" | | 11 | "Every time, the sensation had" | | 12 | "She had tried ignoring it." | | 13 | "She had delivered a curry" | | 14 | "The pendant had kept beating." | | 15 | "She had told herself she" | | 16 | "She had told herself she" | | 17 | "The more honest explanation was" | | 18 | "Tonight it had felt like" | | 19 | "She stepped beyond the light." |
| | ratio | 0.764 | |
| 46.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 216 | | matches | | 0 | "If the pendant reacted to" | | 1 | "Even then, her mother had" |
| | ratio | 0.009 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 4 | | matches | | 0 | "The deep-crimson stone rested beneath her shirt, faintly warm, as though someone had pressed a coal into its heart." | | 1 | "She had delivered a curry to Kensington, returned a stack of containers to the Golden Empress, and sat at the bar downstairs while Silas argued with a televisio…" | | 2 | "Her left wrist began to ache beneath her sleeve, the old crescent scar prickling as if a cold finger traced it." | | 3 | "She remembered being eight years old, falling against a broken gate, her mother shouting for a towel." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "Behind her something whispered (whisper)" | | 3 | "a voice whispered (whisper)" |
| | dialogueSentences | 9 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0.667 | | effectiveRatio | 0.667 | |